Abstract
Metabolite identification is a crucial step in nontargeted metabolomics, but also represents one of its current bottlenecks. Accurate identifications are required for correct biological interpretation. To date, annotation and identification are usually based on the use of accurate mass search or tandem mass spectrometry analysis, but neglect orthogonal information such as retention times obtained by chromatographic separation. While several tools are available for the analysis and prediction of tandem mass spectrometry data, prediction of retention times for metabolite identification are not widespread. Here, we review the current state of retention time prediction in liquid chromatography-mass spectrometry-based metabolomics, with a focus on publications published after 2010.
Highlights
Metabolomics, the systematic study of metabolites in a biological system, has been called “the apogee of the omics trilogy” [1]
In order to get an overview on the employed separation methods, we reviewed studies submitted to Metabolights [29] that were performed with HPLC–MS or UHPLC–MS, and collected columns and solvents used, irrespective of the method being targeted or nontargeted
Eugster et al [35] restrained their model to only CHO-containing natural products, using a training set of 260 compounds and different models based on partial least square regression (PLS) and artificial neural networks (ANN)
Summary
Metabolomics, the systematic study of metabolites in a biological system, has been called “the apogee of the omics trilogy” [1]. MS with different mass resolving power is in frequent use: this includes triple quadrupole (QqQ) MS for targeted, and ToF and Orbitrap MS for nontargeted metabolomic investigations The latter two techniques produce so-called “high-resolution” MS data: high mass resolution allows us to differentiate between ions of almost identical mass; this and the high mass accuracy of the instruments enable non-targeted investigations without prior selection of subsets of metabolites. This facilitates the collection of a comprehensive snapshot of the metabolic state of an organism, cell, or ecosystem and includes the detection of known and unknown metabolites
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